3,571 research outputs found

    Comments on the RGGI Market Design

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    Auctions, carbon auctions, greenhouse gas auctions

    Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

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    This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product

    Dynamic pricing policies for interdependent perishable products or services using reinforcement learning.

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    Many businesses offer multiple products or services that are interdependent, in which the demand for one is often affected by the prices of others. This article considers a revenue management problem of multiple interdependent products, in which dynamically adjusted over a finite sales horizon to maximize expected revenue, given an initial inventory for each product. The main contribution of this article is to use reinforcement learning to model the optimal pricing of perishable interdependent products when demand is stochastic and its functional form unknown. We show that reinforcement learning can be used to price interdependent products. Moreover, we analyze the performance of the Q-learning with eligibility traces algorithm under different conditions. We illustrate our analysis with the pricing of services

    Inventory and pricing management in probabilistic selling

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    Context: Probabilistic selling is the strategy that the seller creates an additional probabilistic product using existing products. The exact information is unknown to customers until they receive the probabilistic products. This strategy is still a relatively new area for both researchers and practitioners. Many of the corresponding operations problems need to be solved to take full advantage of the opportunity of this innovative marketing strategy. However, limited attention has been paid to examining the inventory management of probabilistic selling from the perspective of Operations Management, which cannot meet the needs of decision-making in reality. Objectives: Considering different characteristics of the probabilistic product, the buyer, and the seller involved in probabilistic selling, i.e., the probabilistic product form, the buyers’ behaviours of demand switch and barter exchange, and the seller's product allocation behaviour, we establish models and solve the decision problems of pricing, inventory, joint decision of pricing-inventory, and product allocation, etc. Based on the analysis of optimal decisions and strategy comparison results, we shed some lights on the effectiveness of probabilistic selling on managing uncertainty, and its profitability. Method: First, we analyze the practice scenarios of probabilistic selling. Next we mainly use newsvendor inventory model, hotelling model, and optimization theory to model, solve, and analyze the operational problems. Then we give some analytical results. Next we conduct the numerical analysis using softwares of Matlab and Mathematica. Finally, we provide insightful managerial implications for the practice of probabilistic selling. Results: The thesis derives the optimal operational decisions of inventory order, pricing, inventory allocation, and product line design in probabilistic selling. Overall, the analysis of the results show that probabilistic selling can benefit the seller with higher expected profit by reducing demand/supply uncertainty and improving inventory efficiency. The performance of probabilistic selling is closely dependent on customers' price sensitivity, product similarity, and uncertainty level, etc. Main results considering different research scenarios are as follows: 1) When the price for the probabilistic product is independent on demand reshape, a proper cannibalization can benefit the retailer in terms of yielding a higher expected profit. Probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution strategy outperforms probabilistic selling with higher product similarity. 2) When the price for the probabilistic product is dependent on demand reshape, probabilistic selling can benefit the seller with higher expected profit and lower inventory. Probabilistic selling is more profitable with lower product differentiation, higher customers' price sensitivity, and higher demand uncertainty. Improper pricing would undermine the seller's profit. 3) When the seller offers physical probabilistic product, he can benefit from two effects, namely the risk pooling effect due to demand reshape and the risk diversification effect due to inventory flexibility. 4) When the seller offers barter choice in probabilistic selling, he may benefit from the marketing effect in the barter process. Offering barter choice can broaden the application range of probabilistic selling, which will increase with successful barter probability. Conclusions/Implications: First, the thesis helps sellers understand how to manage their inventory, pricing and related implementation issues to take full advantage of probabilistic selling. Second, this thesis explores the mechanism of this innovative marketing strategy as an inventory management tool to combat uncertainty which also riches the literature on Operations Management, especially inventory management.Antecedentes: Los productos probabilísticos son productos adicionales creados por un proveedor que combina productos existentes y oculta parte de la información del producto. Es decir, cierta información de atributos de los productos probabilísticos es opaca para el cliente. El cliente que compra el producto probabilístico obtiene una de las combinaciones de productos con una cierta probabilidad. Las ventas probabilísticas son una estrategia de ventas que permite la venta de productos probabilísticos. Todavía es un modelo de ventas relativamente nuevo para empresas e investigadores. La implementación de ventas probabilísticas es diversa y aún no se ha verificado la rentabilidad de las diferentes formas de ventas probabilísticas. Se deben abordar las situaciones de inventario y fijación de precios que tengan en cuenta las diferentes realidades. Por el momento, desde la perspectiva de la gestión operativa, existen pocos estudios sobre la toma de decisiones de inventario y fijación de precios bajo el modelo de ventas probabilísticas, que no puede satisfacer las necesidades de las empresas para tomar decisiones científicas en el proceso de implementación. Objetivo: Este documento se centra en los tres actores principales en el proceso de venta probabilística: los productos probabilísticos, compradores y vendedores. Considere el afecto de las diferentes realidades y circunstancias (en concreto, la forma de productos probabilísticos, la demanda de transferencia y el comportamiento de intercambio del comprador, y si el vendedor reemplaza el producto en el proceso de distribución de los productos) sobre la fijación de precios y las decisiones de inventario. Al establecer un modelo que considera los factores realistas antes mencionados, se resuelve el problema de fijación de precios, la decisión conjunta de inventario- precios y la asignación de productos bajo el modelo probabilístico de ventas. Finalmente, a través del análisis de las decisiones y la comparación de estrategias, se obtendrá sugerencias de gestión para la implementación de ventas probabilísticas. Método: En primer lugar, este documento analiza los escenarios de diferentes ventas de probabilidad. En segundo lugar, utilizando el modelo de vendedor de periódicos, el modelo de Hotelling y la teoría de optimización, se intenta resolver y analizar la fijación de precios, el inventario, la toma de decisiones conjunta de inventario-precios y los problemas de decisión de asignación de productos. Luego, da el teorema y analízalo. Finalmente, proporcione asesoramiento de gestión de inventario- precios para los comerciantes que implementan ventas probabilísticas. Conclusión: Este documento ha encontrado las decisiones operativas óptimas para el inventario, fijación de precios, asignación de inventario y diseño de línea de producto en ventas probabilísticas. Los resultados generales muestran que las ventas probabilísticas pueden aumentar la eficiencia del inventario al reducir la incertidumbre de la demanda / oferta, lo que permite a los vendedores obtener mayores ganancias esperadas. El rendimiento de las ventas probabilísticas está estrechamente relacionado con factores tales como la sensibilidad del precio del cliente, la similitud y la incertidumbre del producto. Significado: Primero, permita que los vendedores hagan un buen uso de las ventas probabilísticas. Este artículo los ayuda a comprender cómo resolver problemas de inventario, precios y decisiones operativas relacionadas en modelos de ventas probabilísticas. Segundo, consideramos esta estrategia de marketing innovadora como una herramienta de gestión de inventario, por lo que este documento enriquece la investigación de gestión operativa, especialmente la teoría de gestión de inventarioPostprint (published version

    Amazon and Platform Antitrust

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    With its decision in Ohio v. American Express, the U.S. Supreme Court for the first time embraced the recently developed, yet increasingly prolific, concept of the two-sided platform. Through advances in technology, platforms, which serve as intermediaries allowing two groups to transact, are increasingly ubiquitous, and many of the biggest tech companies operate in this fashion. Amazon Marketplace, for example, provides a platform for third-party vendors to sell directly to consumers through Amazon’s web and mobile interfaces. At the same time that platforms and their scholarship have evolved, a burgeoning antitrust movement has also developed which focuses on the impact of the dominance of these tech companies and the fear that current antitrust laws are ill-equipped to prevent any potential anticompetitive behavior. Many of those who feel this way worried that American Express, which decided whether a plaintiff alleging anticompetitive behavior by a two- sided platform would have to show harm to both sides of the market to make a prima facie case, would give companies like Amazon even more power. This Note argues that while the case could be interpreted in such a way, because Amazon and similarly situated platforms possess a great degree of control over their users—in some cases competing with them directly—it would be unwise to do so
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